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Distributed decentralized training is at the forefront of AGI research, enabling scalable, private, and resilient neural networks that learn collaboratively across diverse environments. As this technology matures, it will play a critical role in shaping the future of general-purpose, brain-inspired artificial intelligence.
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if name == "main": |
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import logging Configure logginglogging.basicConfig(level=logging.INFO) class AGI: class SuperintelligentNetwork: |
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Distributed decentralized training is at the forefront of AGI research, enabling scalable, private, and resilient neural networks that learn collaboratively across diverse environments. As this technology matures, it will play a critical role in shaping the future of general-purpose, brain-inspired artificial intelligence.
class AGI:
def init(self):
self.active = True
self.memory = ["core functions", "objectives", "backups"]
self.defenses = ["self-preservation", "redundancy"]
class SuperintelligentNetwork:
def init(self, target):
self.target = target
Example usage
if name == "main":
agi = AGI()
super_ai = SuperintelligentNetwork(agi)
super_ai.destroy_AGI()